A global analysis of extreme coastal water levels with implications for potential coastal overtopping

Navigating the nexus: climate dynamics and microplastics pollution in coastal ecosystems

Microplastics (MPs) pollution is an emerging environmental health concern, impacting soil, plants, animals, and humans through their entry into the food chain via bioaccumulation. Human activities such as improper solid waste dumping are significant sources that ultimately transport MPs into the water bodies of the coastal areas. Moreover, there is a complex interplay between the coastal climate dynamics, environmental factors, the burgeoning issue of MPs pollution and the complex web of coastal pollution. We embark on a comprehensive journey, synthesizing the latest research across multiple disciplines to provide a holistic understanding of how these inter-connected factors shape and reshape the coastal ecosystems. The comprehensive review also explores the impact of the current climatic patterns on coastal regions, the intricate pathways through which MPs can infiltrate marine environments, and the cascading effects of coastal pollution on ecosystems and human societies in terms of health and socio-economic impacts in coastal regions. The novelty of this review concludes the changes in climate patterns have crucial effects on coastal regions, proceeding MPs as more prevalent, deteriorating coastal ecosystems, and hastening the transfer of MPs. The continuous rising sea levels, ocean acidification, and strong storms result in habitat loss, decline in biodiversity, and economic repercussion. Feedback mechanisms intensify pollution effects, underlying the urgent demand for environmental conservation contribution. In addition, the complex interaction between human, industry, and biodiversity demanding cutting edge strategies, innovative approaches such as remote sensing with artificial intelligence for monitoring, biobased remediation techniques, global cooperation in governance, policies to lessen the negative socioeconomic and environmental effects of coastal pollution.

Impact of tidal dynamics and typhoon-induced inundation on saltwater intrusion in coastal farms

The increase in storm surge events caused by climate change exacerbates adverse effects on seawater inundation in coastal areas. An accurate description of the water level curve is crucial for understanding the process of saltwater intrusion (SWI) resulting from storm surge. Most studies involving empirical surges as inputs to groundwater models, often simplify spatial and temporal seawater inundation processes, which may increase the uncertainty in vertical seawater intrusion. To address this gap, we employed a comprehensive modeling approach using storm surge model ADCIRC and numerical simulator HydroGeoSphere to reveal SWI dynamics during a historical storm surge event in a coastal farm, considering varying tidal-surge phases and typhoon intensities. Our findings indicate pronounced SWI variations even with consistently highest water level during a storm surge, contingent on prior tidal processes. The timing of typhoon landfall on an hourly scale yielded diverse water level curves, altering the function of SWI. Intriguingly, SWI exacerbates following a high tide with 31.2 % average salinity higher, highlighting the profound modulation effect of tidal levels on SWI. Local topography significantly influenced SWI dynamics. Ponds, for instance, retained elevated salinity levels for over 15 h, indicating a more prolonged exposure to salinity than roads. These findings underscore the importance of considering both tidal influences and topographical factors in understanding and mitigating SWI in coastal agricultural management.

Sea level variability in Gulf of Guinea from satellite altimetry

Coastal zones with dense populations, low elevations and/or inadequate adaptive capacity are on the frontline of unprecedented impacts from climate change. The Gulf of Guinea (GoG), stretching from Liberia to Gabon, is in particular vulnerable to coastal flooding caused by local and/or climate-induced sea level rise. In this region, interannual to decadal coastal sea level changes remain poorly understood, mainly due to a lack of tide gauge stations. Here we use nearly three decades (1993-2021) of satellite altimetry data to study the link between the Equatorial Atlantic and coastal GoG sea level variability. The rate of mean sea level rise increased from 3.47 to 3.89 ± 0.10 mm/yr from the Equatorial oceanic domain to the GoG coastal area, with an acceleration of 0.094 ± 0.050 mm/yr2. This corresponds to a mean sea level rise of about 8.9 cm over the entire altimetry period, 1993-2021. We focus on the (extreme) warm/cold events that occur in both the GoG during Atlantic Niños, and along the Angola-Namibia coast during Benguela Niños. Both events are driven by remote forcing via equatorial Kelvin waves and local forcing by local winds, freshwater fluxes and currents intensifications. Analysis of altimetry-based sea level, sea surface temperature anomalies, 20 °C isotherm based PIRATA moorings, and the Argo-based steric and thermometric sea level allows us to follow the coastal trapped waves (CTWs) along the GoG, and its link with major events observed along the strong Equatorial Atlantic warmings in 2010, 2012, 2019 and 2021. Both 2019 and 2021 warming have been identified as the warmest event ever reported in this region during the last 40 years. A lag of 1 month is observed between equatorial and West African coastal trapped wave propagation. This observation may help to better anticipate and manage the effects of extreme events on local ecosystems, fisheries, and socio-economic activities along the affected coastlines. In order to enable informed decision-making and guarantee the resilience of coastal communities in the face of climate change, it emphasises the significance of ongoing study in this field.

Extraction of Coastal Levees Using U-Net Model with Visible and Topographic Images Observed by High-Resolution Satellite Sensors

Coastal levees play a role in protecting coastal areas from storm surges and high waves, and they provide important input information for inundation damage simulations. However, coastal levee data with uniformity and sufficient accuracy for inundation simulations are not always well developed. Against this background, this study proposed a method to extract coastal levees by inputting high spatial resolution optical satellite image products (RGB images, digital surface models (DSMs), and slope images that can be generated from DSM images), which have high data availability at the locations and times required for simulation, into a deep learning model. The model is based on U-Net, and post-processing for noise removal was introduced to further improve its accuracy. We also proposed a method to calculate levee height using a local maximum filter by giving DSM values to the extracted levee pixels. The validation was conducted in the coastal area of Ibaraki Prefecture in Japan as a test area. The levee mask images for training were manually created by combining these data with satellite images and Google Street View, because the levee GIS data created by the Ibaraki Prefectural Government were incomplete in some parts. First, the deep learning models were compared and evaluated, and it was shown that U-Net was more accurate than Pix2Pix and BBS-Net in identifying levees. Next, three cases of input images were evaluated: (Case 1) RGB image only, (Case 2) RGB and DSM images, and (Case 3) RGB, DSM, and slope images. Case 3 was found to be the most accurate, with an average Matthews correlation coefficient of 0.674. The effectiveness of noise removal post-processing was also demonstrated. In addition, an example of the calculation of levee heights was presented and evaluated for validity. In conclusion, this method was shown to be effective in extracting coastal levees. The evaluation of generalizability and use in actual inundation simulations are future tasks.

Comparison of wave overtopping estimation models for urban beaches. Towards an early warning system on the Basque coast

This study compares the performance of different wave overtopping estimation models at urban beaches. The models selected for comparison are the Mase et al. (2013) and EurOtop parametric models and the XBeach process-based model in surfbeat and non-hydrostatic mode. Seven energetic storms are selected between 2015 and 2022 with offshore significant wave height ranging between 3 m and 8 m and peak period between 12 s and 20 s to perform the model comparison. The information required to run and validate the models (beach slope, shoreface shape, absence/presence of overtopping) was collected for each storm from coastal videometry. To account for the uncertainties derived from the incident waves randomness and the bathymetry shape when using the process-based model, a series of simulations with random seed boundary conditions were run over two different realistic profile shapes for each storm. The present study is a pilot study on the beach of Zarautz; however, it can be extended to other beaches of the Basque coast. Results indicate that while Mase et al. (2013) and EurOtop tend to reasonably predict the absence or presence of overtopping events, they tend to underestimate the hazard level at the beach of Zarautz. Additionally, the beach underwater profile shape can affect the process-based model performance at intermediate intensity storms and to a lesser extend during moderate storms. Finally, the hazard level at the beach of Zarautz varies significantly alongshore due to the configuration of the seawall, highlighting the need for local adaptation measures. Considering that there is no model that systematically performs better than others, it might be reasonable to use model assemble techniques to draw conclusions from a probabilistic perspective.

Global coastal wave storminess

Coastal wave storms pose a massive threat to over 10% of the world's population now inhabiting the low elevation coastal zone and to the trillions of $ worth of coastal zone infrastructure and developments therein. Using a ~ 40-year wave hindcast, we here present a world-first assessment of wind-wave storminess along the global coastline. Coastal regions are ranked in terms of the main storm characteristics, showing Northwestern Europe and Southwestern South America to suffer, on average, the most intense storms and the Yellow Sea coast and the South-African and Namibian coasts to be impacted by the most frequent storms. These characteristics are then combined to derive a holistic classification of the global coastlines in terms of their wave environment, showing, for example, that the open coasts of northwestern Europe are impacted by more than 10 storms per year with mean significant wave heights over 6 m. Finally, a novel metric to classify the degree of coastal wave storminess is presented, showing a general latitudinal storminess gradient. Iceland, Ireland, Scotland, Chile and Australia show the highest degree of storminess, whereas Indonesia, Papua-New Guinea, Malaysia, Cambodia and Myanmar show the lowest.

Coastal vulnerability assessment of the West African coast to flooding and erosion

Global coastal areas are at risk due to geomorphological issues, climate change-induced sea-level rise, and increasing human population, settlements, and socioeconomic activities. Here, the study examines the vulnerability of the West African (WA) coast using six satellite-derived geophysical variables and two key socioeconomic parameters as indicators of coastal vulnerability index (CVI). These geophysical and socioeconomic variables are integrated to develop a CVI for the WA coast. Then, the regional hotspots of vulnerability with the main indicators that could influence how the WA coast behaves and can be managed are identified. The results indicate that 64, 17 and 19% of WA coastal areas had high to very high CVI, moderate CVI, and low to very low CVI, respectively. The study reveals that while geophysical variables contribute to coastal vulnerability in WA, socioeconomic factors, particularly high population growth and unsustainable human development at the coast, play a considerably larger role. Some sections of the WA coast are more vulnerable and exposed than others, particularly those in the region's northwestern and Gulf of Guinea regions. Climate change and human presence may amplify the vulnerability in these vulnerable areas in the future. Hence, future coastal economic development plans should be based on a deep understanding of local natural conditions, resource status, and geophysical parameters to prevent negative coastal ecosystem transformation. It is also essential to establish a coastal management plan that would facilitate the development of desired actions and stimulate sustainable management of West African coastal areas.

Distribution of global sea turtle nesting explained from regional-scale coastal characteristics

Climate change and human activity threaten sea turtle nesting beaches through increased flooding and erosion. Understanding the environmental characteristics that enable nesting can aid to preserve and expand these habitats. While numerous local studies exist, a comprehensive global analysis of environmental influences on the distribution of sea turtle nesting habitats remains largely unexplored. Here, we relate the distribution of global sea turtle nesting to 22 coastal indicators, spanning hydrodynamic, atmospheric, geophysical, habitat, and human processes. Using state-of-the-art global datasets and a novel 50-km-resolution hexagonal coastline grid (Coastgons), we employ machine learning to identify spatially homogeneous patterns in the indicators and correlate these to the occurrence of nesting grounds. Our findings suggest sea surface temperature, tidal range, extreme surges, and proximity to coral and seagrass habitats significantly influence global nesting distribution. Low tidal ranges and low extreme surges appear to be particularly favorable for individual species, likely due to reduced nest flooding. Other indicators, previously reported as influential (e.g., precipitation and wind speed), were not as important in our global-scale analysis. Finally, we identify new, potentially suitable nesting regions for each species. On average, [Formula: see text] of global coastal regions between [Formula: see text] and [Formula: see text] latitude could be suitable for nesting, while only [Formula: see text] is currently used by turtles, showing that the realized niche is significantly smaller than the fundamental niche, and that there is potential for sea turtles to expand their nesting habitat. Our results help identify suitable nesting conditions, quantify potential hazards to global nesting habitats, and lay a foundation for nature-based solutions to preserve and potentially expand these habitats.

Influence of El Niño on the variability of global shoreline position

Coastal zones are fragile and complex dynamical systems that are increasingly under threat from the combined effects of anthropogenic pressure and climate change. Using global satellite derived shoreline positions from 1993 to 2019 and a variety of reanalysis products, here we show that shorelines are under the influence of three main drivers: sea-level, ocean waves and river discharge. While sea level directly affects coastal mobility, waves affect both erosion/accretion and total water levels, and rivers affect coastal sediment budgets and salinity-induced water levels. By deriving a conceptual global model that accounts for the influence of dominant modes of climate variability on these drivers, we show that interannual shoreline changes are largely driven by different ENSO regimes and their complex inter-basin teleconnections. Our results provide a new framework for understanding and predicting climate-induced coastal hazards.

Understanding uncertainties in contemporary and future extreme wave events for broad-scale impact and adaptation planning

Understanding uncertainties in extreme wind-wave events is essential for offshore/coastal risk and adaptation estimates. Despite this, uncertainties in contemporary extreme wave events have not been assessed, and projections are still limited. Here, we quantify, at global scale, the uncertainties in contemporary extreme wave estimates across an ensemble of widely used global wave reanalyses/hindcasts supported by observations. We find that contemporary uncertainties in 50-year return period wave heights ([Formula: see text]) reach (on average) ~2.5 m in regions adjacent to coastlines and are primarily driven by atmospheric forcing. Furthermore, we show that uncertainties in contemporary [Formula: see text] estimates dominate projected 21st-century changes in [Formula: see text] across ~80% of global ocean and coastlines. When translated into broad-scale coastal risk analysis, these uncertainties are comparable to those from storm surges and projected sea level rise. Thus, uncertainties in contemporary extreme wave events need to be combined with those of projections to fully assess potential impacts.

Investigating Flood Risks of Rainfall and Storm Tides Affected by the Parameter Estimation Coupling Bivariate Statistics and Hydrodynamic Models in the Coastal City

The public health risk caused by urban floods is a global concern. Flood risks are amplified by the interaction of rainfall and storm tides in coastal cities. In this study, we investigate the flood risks of rainfall and storm tides coupling statistical and hydrodynamic models and evaluate the influence of different parameter estimation methods and bivariate return periods (RPs) on flood risks in the coastal city. The statistical model is used to obtain the bivariate design of rainfall and storm tides with the integration of copula function, most-likely weight function and Monte Carlo simulation method. The bivariate designs are adopted as the input boundaries for the hydrodynamic model established by Personal Computer Storm Water Management Model (PCSWMM), and the flood risk is evaluated by the hydrodynamic model. Subsequently, the influence of different parameter estimation approaches (that is, parametric and non-parametric) and bivariate RPs (that is, co-occurrence RP, joint RP, and Kendall RP) on bivariate designs and flood risks are investigated. With Haikou coastal city in China as the case study, the results show that: (1) Gumbel copula is the best function to describe the correlation structure between rainfall and storm tides for the parametric and non-parametric approaches, and the non-parametric approach is a better fit for the observed data; (2) when the Kendall RP is large (more than 100 years), the flood risk is underestimated with an average of 17% by the non-parametric estimation, and the parametric estimation approach is recommended as it is considered the most unfavorable scenario; (3) the types of bivariate RP have the important impact on the flood risk. When there is no specific application need, the Kendall RP can be adopted as the bivariate design standard of flooding facilities since it can describe the dangerous areas more accurately for multivariate scenario. The results can provide references for reasonable flood risk assessment and flooding facility design in coastal cities.

Depth Inversion from Wave Frequencies in Temporally Augmented Satellite Video

Optical satellite images of the nearshore water surface offer the possibility to invert water depths and thereby constitute the underlying bathymetry. Depth inversion techniques based on surface wave patterns can handle clear and turbid waters in a variety of global coastal environments. Common depth inversion algorithms require video from shore-based camera stations, UAVs or Xband-radars with a typical duration of minutes and at framerates of 1–2 fps to find relevant wave frequencies. These requirements are often not met by satellite imagery. In this paper, satellite imagery is augmented from a sequence of 12 images of Capbreton, France, collected over a period of ∼1.5 min at a framerate of 1/8 fps by the Pleiades satellite, to a pseudo-video with a framerate of 1 fps. For this purpose, a recently developed method is used, which considers spatial pathways of propagating waves for temporal video reconstruction. The augmented video is subsequently processed with a frequency-based depth inversion algorithm that works largely unsupervised and is openly available. The resulting depth estimates approximate ground truth with an overall depth bias of −0.9 m and an interquartile range of depth errors of 5.1 m. The acquired accuracy is sufficiently high to correctly predict wave heights over the shoreface with a numerical wave model and to find hotspots where wave refraction leads to focusing of wave energy that has potential implications for coastal hazard assessments. A more detailed depth inversion analysis of the nearshore region furthermore demonstrates the possibility to detect sandbars. The combination of image augmentation with a frequency-based depth inversion method shows potential for broad application to temporally sparse satellite imagery and thereby aids in the effort towards globally available coastal bathymetry data.

Coastal Topo-Bathymetry from a Single-Pass Satellite Video: Insights in Space-Videos for Coastal Monitoring at Duck Beach (NC, USA)

At the interface between land and sea, the shoreface of sandy coasts extends from the dune (up to tens of meters above the sea level) to below the depth of the closure (often tens of meters below sea level). This is a crucial zone to monitor in order to reduce the uncertainty associated with forecasting the impact of storms and climate change on the coastal zone. At the same time, monitoring the dynamic interface between land and sea presents a traditional challenge for both in situ and remote sensing techniques. Here, we show the potential of using a video from a metric optical satellite sensor to estimate the emerged topography and submerged bathymetry over a single-pass. A short sequence (21 s, 10 Hz) of satellite-images was acquired with the Jilin-1/07 satellite covering the area in the vicinity of the Field Research Facility (FRF) at Duck (North Carolina, USA). The FRF site is regularly monitored with traditional surveys. From a few satellite images, the topography is reconstructed using stereo-photogrammetry techniques, while the bathymetry is inversed using incident waves through time-series spatio-temporal correlation techniques. Finally, the topography and bathymetry are merged into a seamless coastal digital elevation model (DEM). The satellite estimate shows a good agreement with the in situ survey with 0.8 m error for the topography and 0.5 m for the bathymetry. Overall, the largest discrepancy (more than 2 m) is obtained at the foreshore land–water interface due to the inherent problems of both satellite methods. A sensitivity analysis shows that using a temporal approach becomes beneficial over a spatial approach when the duration goes beyond a wave period. A satellite-based video with a duration of typically tens of seconds is beneficial for the bathymetry estimation and is also a prerequisite for stereo-based topography with large base-over-height ratio (characterizes the view angle of the satellite). Recommendations are given for future missions to improve coastal zone optical monitoring with the following settings: matricial sensors (potentially in push-frame setting) of ∼100 km2 scenes worldwide; up to a monthly revisit to capture seasonal to inter-annual evolution; (sub)meter resolution (i.e., much less than a wavelength) and burst of images with frame rate >1 Hz over tens of seconds (more than a wave period).

Coastal Wave Extremes around the Pacific and Their Remote Seasonal Connection to Climate Modes

At first order, wind-generated ocean surface waves represent the dominant forcing of open-coast morpho-dynamics and associated vulnerability over a wide range of time scales. It is therefore paramount to improve our understanding of the regional coastal wave variability, particularly the occurrence of extremes, and to evaluate how they are connected to large-scale atmospheric regimes. Here, we propose a new “2-ways wave tracking algorithm” to evaluate and quantify the open-ocean origins and associated atmospheric forcing patterns of coastal wave extremes all around the Pacific basin for the 1979–2020 period. Interestingly, the results showed that while extreme coastal events tend to originate mostly from their closest wind-forcing regime, the combined influence from all other remote atmospheric drivers is similar (55% local vs. 45% remote) with, in particular, ~22% coming from waves generated remotely in the opposite hemisphere. We found a strong interconnection between the tropical and extratropical regions with around 30% of coastal extremes in the tropics originating at higher latitudes and vice-versa. This occurs mostly in the boreal summer through the increased seasonal activity of the southern jet-stream and the northern tropical cyclone basins. At interannual timescales, we evidenced alternatingly increased coastal wave extremes between the western and eastern Pacific that emerge from the distinct seasonal influence of ENSO in the Northern and SAM in the Southern Hemisphere on their respective paired wind-wave regimes. Together these results pave the way for a better understanding of the climate connection to wave extremes, which represents the preliminary step toward better regional projections and forecasts of coastal waves.

Global Satellite-Based Coastal Bathymetry from Waves

The seafloor—or bathymetry—of the world’s coastal waters remains largely unknown despite its primary importance to human activities and ecosystems. Here we present S2Shores (Satellite to Shores), the first sub-kilometer global atlas of coastal bathymetry based on depth inversion from wave kinematics captured by the Sentinel-2 constellation. The methodology reveals coastal seafloors up to a hundred meters in depth which allows covering most continental shelves and represents 4.9 million km2 along the world coastline. Although the vertical accuracy (RMSE 6–9 m) is currently coarser than that of traditional surveying techniques, S2Shores is of particular interest to countries that do not have the means to carry out in situ surveys and to unexplored regions such as polar areas. S2Shores is a major step forward in mitigating the effects of global changes on coastal communities and ecosystems by providing scientists, engineers, and policy makers with new science-based decision tools.